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Fast Fourier Transform (FFT) is Discrete Fourier Transform (DFT) algorithm. To perform FFT in Python you need to install several packages/modules/libraries. You can skip this portion if you already have it available. I am a windows user so I will show how to setup your machine for windows environment. This is an effort to provide the easiest and extensive tutorial for image processing beginners in python. Things you need to download and install are as follows: 1. Install Python 2.7.10 (as for Python 3.4 you will face challenges for other packages) from https://www.python.org/downloads/ 2. Install SciPy 0.16.0 superpack for Python 2.7 from http://sourceforge.net/projects/scipy/files/scipy/0.16.0/ 3. Install NumPy 1.9.2 superpack for Python 2.7 from http://sourceforge.net/projects/numpy/files/NumPy/1.9.2/ 4. Install Six 1.9.0 from https://pypi.python.org/pypi/six 5. Install Matplotlib 1.4.3 for Python 2.7 from http://sourceforge.net/projects/matplotlib/files/matplotlib/...

Aim: Plot sine wave with 5 cycles. Code: import numpy as np import matplotlib.pyplot as plt t = np.linspace(0, 10*np.pi, 1024, endpoint=True) s = np.sin(t) plt.plot(t,s) plt.show() Output:

DEEP LEARNING LAB SERIES SCHEDULE: [Click the respective link to open the folder] 7/22 Class #1 - Introduction to Deep Learning 7/29 Office Hours for Class #1 8/5 Class #2 - Getting Started with DIGITS interactive training system for image classification 8/12 Office Hours for Class #2 8/19 Class #3 - Getting Started with the Caffe Framework 8/26 Office Hours for Class #3 9/2 Class #4 - Getting Started with the Theano Framework 9/9 Office Hours for Class #4 9/16 Class #5 - Getting Started with the Torch Framework 9/23 Office Hours for Class #5 It is an introductory course (free online webinar) on Deep learning which is a part of Machine learning which is a part of Artificial intelligence. More information available at developer.nvidia.com/deep-learning-courses Useful links: Caffe: http://caffe.berkeleyvision.org/ Theano: http://deeplearning.net/software/theano/ Torch: http://torch.ch/ DIGITS: https://developer.nvid...

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